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Öğe Design and Analysis of Cpm and Cpmk Indices for Uncertainty Environment by Using Pythagorean Fuzzy Sets(Institute of Electrical and Electronics Engineers Inc., 2022) Yalcin S.; Kaya I.Process capability analysis (PCA) is a statistical analysis tool to examine variability of the process that causes faults for outputs and reduces customer satisfaction level. So, it is a completely effective method to improve the process' quality. One of the effective methods is process capability indices (PCIs) that are used to analyze the capability of any process by using specification limits (SLs) and process' variation. Especially in real case problems, there are many factors that causing uncertainty for the process. Although traditional PCIs are effective tools to analyze variation of process, they caused some misleading results and incorrect interpretations when the process has uncertainties. To overcome the problem, the PCIs have been re-designed under uncertainty to increase their effectiveness by using fuzzy sets (FSs). In recently, some fuzzy set extensions (FSEs) have been derived to deal with uncertainty and they can model uncertainties of process more effectively. In this paper, Pythagorean Fuzzy Sets (PFSs), one of the most common FSEs, are used to analyze process capability bu improving some PCIs based on PFSs. For this aim, generally used PCIs called Cpm and Cpmk are re-designed by using PFSs as the first time in the literature. The mathematical structures of these two indices are re-formulated and PCIs based on PFSs (PFPCIs) have been derived. Additionally, an application related with dimensions of a gear for a piston is also applied to analyze usage of proposed PFPCIs. The obtained results confirmed that the indices Cpm and Cpmk based on PFSs are more capable for modelling uncertainty and give more information and have more sensitiveness than the traditional PCIs. © 2022 IEEE.Öğe A fuzzy based model proposal on risk analysis for human-robot interactive systems(Institute of Electrical and Electronics Engineers Inc., 2022) Bozkus E.; Kaya I.; Yakut M.The role and job descriptions of the new generation of industrial robots that will operate in smart factories are being shaped by the industry 4.0 (I4.0) process, which has evolved with digital transformation and advanced production procedures. Human-robot interaction is a new industry trend and a key component of the I4.0 strategy. The main objective of this new solution is to improve the safety, ergonomics, productivity, and quality of the process. This solution aims to bridge the gap between manual production and fully automated production. In this way, the employee integrates the advantage of both humans and robots by sharing the workspace with the robot in non-ergonomic, repetitive, uncomfortable, and dangerous operations. This also means that the inclusion of robots in manufacturing processes does not devalue the human component; on the contrary, it shows that the increase in productivity is due to human-robot cooperation. As the level of human-robot cooperation increases, production capacity must be waived as a result of the slowdown of robots by nature, and risk assessment becomes more important according to certain standards. It is also clear that risk analysis of human and robot interaction systems contains a mixture of quantitative and qualitative data based on human evaluations and hesitancy and process uncertainties. In general, risk assessment approaches rely on the expertise and experience of specialists. So, the fuzzy set theory (FST) is more suitable to evaluate the risk assessment of this system. This study aims to contribute to improving human-robot collaboration and safety in an industrial setting for risk assessment based on FST. Additionally, the z-number, which is a fuzzy number of pairs is integrated into the proposed methodology to reflect the uncertainties of the risk assessment stage. Within the scope of the study, a new fuzzy-based risk assessment methodology is proposed to provide a safe workplace where humans and robots collaborate on a typical task. The proposed methodology consists of DELPHI, DEMATEL, ANP, and VIKOR which are multi-criteria decisions making (MCDM) methods based on the z-numbers that can take into account the uncertainty of the data and the hesitancies of the experts. © 2022 IEEE.Öğe A Two-Dimensional Fuzzy Risk Assessment Model for Occupational Health and Safety Evaluations(Institute of Electrical and Electronics Engineers Inc., 2022) Yakut M.; Kaya I.; Bozkus E.In working life, the danger in the environment and the risks arising from it are of great importance for the health and safety of the employees. In order for these pre-determined damaging factors to be evaluated correctly, an evaluation method suitable for that field of activity should be selected. Since real case risk assessment problems include many uncertainties, the fuzzy set theory (FST) that has a huge ability to model uncertainty has found a wide application area thanks to its easy and convenient solution approach to the solution of difficult and complex problems today. FST can be used to eliminate the uncertainties in classical risk analyzes in workplaces and to introduce new methods by applying different combinations. Combinations prepared using various methods make positive contributions to occupational health and safety (OHS) risk assessment processes. These combinations prepared using various methods. In this paper, one of fuzzy set extensions named Z-number has been integrated with the proposed methodology to improve ability that is modelling uncertainties. It ensures that the inadequacies in the existing risk assessment methods are eliminated. For this aim a new framework has been suggested. In the proposed model that we will propose in our study, the risks arising from the danger will be analyzed by using Fine Kinney Method that is one of the most widely used methods in OHS risk analysis. Grading of risks in the Fine Kinney method is obtained by multiplying the probability of occurrence of the risks, the frequency of exposure to the hazard and the numerical values of the effect it creates. Additionally, the proposed methodology consists of three MCDM methods such as DEMATEL, AHP or ANP and TOPSIS methodology. The DEMATEL method will be used to establish causality between hazards. The method AHP or ANP will be used to determine risk weights and the TOPSIS method will be used to determine hazards in order of priority. The proposed framework is also being constructed on Z-numbers and thus a new risk assessment methodology based on MCDM, and Fine-Kinney Methods is suggested. It will be advantageous to use Z-numbers to clarify the uncertainties. to ensure that risk assessments are more objective and to make its applicability even more possible. The proposed framework can be applied in a real case risk assessment problem to analyze its results. © 2022 IEEE.Öğe Two-Dimensional Uncertainty Analysis for Cp and Cpk Process Capability Indices(Institute of Electrical and Electronics Engineers Inc., 2022) Yalcin S.; Kaya I.Process capability analysis (PCA) is an efficient statistical technique for calculating of process' ability to meet predetermined specification limits (SLs) that defined by customer, engineers or designers. Measurements and evaluations for PCA may be vague, incomplete or inaccurate in the real-case problems. In that cases, the process capability should be successfully measured by using fuzzy set extensions to model uncertainties of the process. One of fuzzy set extensions named Pythagorean fuzzy Sets (PFSs) that also contains the non-membership function can be employed as an effective tool to model uncertainty better than traditional fuzzy sets (TFSs). In this paper, a novel approach based on PFSs is suggested to increase flexibility and sensitivity of the PCA and to successfully model the uncertainties. For this aim, two of frequently used process capability indices (PCIs) Cp and Cpk, are analyzed based on PFSs. Then, the Pythagorean fuzzy process capability indices (PFPCIs) have been derivate respectively for the indices Cp and Cpk and the mathematical backgrounds of these indices have been developed for the first time in the literature. Additionally, the proposed indices Cp and Cpk have been applied to a real case problem from manufacturing industry. The obtained PCIs based on PFSs provide some additional flexibility and information about the process since they better modeled process uncertainty. Moreover, it is demonstrated that the proposed PFCPIs can be effectively applied on process to manage PCA. © 2022 IEEE.
